Experiences, best practice, pitfalls and challengesDTU Wind Energy

03 November 2016DTU Wind Energy, Technical University of Denmark

WASA fundamentals and guiding principles

• Public domain [and free]–67 966 data downloads and 4 800 other downloads

• Traceable and transparent [methodology]

• Industry-standard [tools and procedures]

• Uncertainties assessed [to the extent possible]

• Platform for future development [research based]

Results of the Wind Atlas for South Africa (WASA)

WASA Project Team, 8 April 2014, Cape Town

This is exactly what is needed for educational purposes!

Experiences and best practice2

03 November 2016DTU Wind Energy, Technical University of Denmark

WASA data in education WASA data and materials used for:

• Course exercises

• Course project work

• Master thesis work

• Special course reports

• Teaching materials

• Lectures and talks

• Fundamental for grid planning

• Validation of models– boundary-layer theory– microscale models– mesoscale models– Global Wind Atlas– and many more

Experiences and best practice3

03 November 2016DTU Wind Energy, Technical University of Denmark

Experiences• Data easily accessible and free

• Few copyright considerations

• WASA data are ‘real data’

• Easy to mimic real-life projects

• State-of-the-art data quality

• WASA project well documented

• Tools and software available

• WASA part of wider tradition

• SA climatology challenging

⇒WASA data are excellent for teaching and project work

Experiences and best practice4

03 November 2016DTU Wind Energy, Technical University of Denmark

Best practiceEngineering best practice

• IEC standards

• Measnet guidelines

• WAsP best practices

• WRF best practices

• WASA R&D etc.

Educational best practice

• Well-described methodology

• Lots of material available

• Continuing education too

Experiences and best practice5

03 November 2016DTU Wind Energy, Technical University of Denmark

Pitfalls and challenges• Mean statistics and pdf’s

– Wind power is distributions– Meteorology / climatology– Prognostic / diagnostic

• Reality and model world– Measurements and modelling– Model operational envelopes– Model resolution

• Engineering best practice– Sensitivity analyses– Uncertainty assessment– Reporting practices

• Implementation for teaching– Few challenges

Experiences and best practice6

03 November 2016DTU Wind Energy, Technical University of Denmark

Final remarks about uncertainty• Knowledge of the surface data is very important

Experiences and best practice7